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Cited by 48Maritime Navigation and SafetyIoT and Edge/Fog ComputingWater Quality Monitoring Technologies

Autonomous marine robot based on AI recognition for permanent surveillance in marine protected areas

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Molina-Molina, J. Carlos, Salhaoui, Marouane, Guerrero-González, Antonio, Arioua, Mounir·2021·Sensors
DOI 10.3390/s21082664PMC PMC8070409
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Abstract

The world’s oceans are one of the most valuable sources of biodiversity and resources on the planet, although there are areas where the marine ecosystem is threatened by human activities. Marine protected areas (MPAs) are distinctive spaces protected by law due to their unique characteristics, such as being the habitat of endangered marine species. Even with this protection, there are still illegal activities such as poaching or anchoring that threaten the survival of different marine species. In this context, we propose an autonomous surface vehicle (ASV) model system for the surveillance of marine areas by detecting and recognizing vessels through artificial intelligence (AI)-based image recognition services, in search of those carrying out illegal activities. Cloud and edge AI computing technologies were used for computer vision. These technologies have proven to be accurate and reliable in detecting shapes and objects for which they have been trained. Azure edge and cloud vision services offer the best option in terms of accuracy for this task. Due to the lack of 4G and 5G coverage in offshore marine environments, it is necessary to use radio links with a coastal base station to ensure communications, which may result in a high response time due to the high latency involved. The analysis of on-board images may not be sufficiently accurate; therefore, we proposed a smart algorithm for autonomy optimization by selecting the proper AI technology according to the current scenario (SAAO) capable of selecting the best AI source for the current scenario in real time, according to the required recognition accuracy or low latency. The SAAO optimizes the execution, efficiency, risk reduction, and results of each stage of the surveillance mission, taking appropriate decisions by selecting either cloud or edge vision models without human intervention.

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Cited by

Sorted by citations

Authors
Molina-Molina, J. C., Salhaoui, M., Guerrero-González, A., & Arioua, M.
Affiliations
  • Department of Automation, Electrical Engineering and Electronic Technology, Universidad Politécnica de Cartagena, Plaza del Hospital 1, 30202 Cartagena, Spain
  • Abdelmalek Essaâdi University (MA)
ORCID
Venue
Sensors
Publisher
MDPI AG
Published
2021-04-10
Volume
21
Issue
8
Pages
2664
ISSN
1424-8220 (ISSN-L)
DOI
10.3390/s21082664
PMC
PMC8070409
OpenAlex
W3154800543
Cited by
48
References
3
Citations by year
2026: 7 · 2025: 13 · 2024: 15 · 2023: 6 · 2022: 5 · 2021: 2
FWCI
3.60
Type
journal article
Language
en
Open access
Licence
Locations
  • Sensors (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • Consejería de Agua, Agricultura, Ganadería, Pesca y Medio Ambiente, Comunidad Autónoma de la Región de Murcia (PROYECTO VIGIA: SISTEMA DE VIGILANCIA COSTERO BASADO EN VEHÍCULOS AUTÓNOMOS DE SUPERFICIE.)
  • Consejería de Empleo, Universidades, Empresa y Medio Ambiente, Dirección General de Comercio, Consumo y Simplificación administrativa (PROYECTO OOMMUR: OBSERVATORIO OCEANOGRÁFICO MÓVIL DE LA REGIÓN DE MURCIA)
Topics
  • Physical Sciences › Engineering › Ocean Engineering › Maritime Navigation and Safety
  • Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
  • Physical Sciences › Environmental Science › Water Science and Technology › Water Quality Monitoring Technologies
Keywords
unmanned surface vehicles, marine protected areas, maritime surveillance, ship detection, image recognition, real-time decision making, poaching detection, computer vision
MeSH terms
Artificial Intelligence, Conservation of Natural Resources, Humans, Oceans and Seas, Robotics, Ecosystem, Biodiversity

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